• The Journal of Korean Society of Virology
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• v.27 no.2
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• pp.239-255
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• 1997

Expression of the cDNA of the VP1 gene on the genome RNA B segment of infectious pancreatic necrosis virus (IPNV) DRT strain in E. coli and a recombinant baculovirus were carried out. The VP1 gene in the pMal-pol clone (Lee et al. 1995) was cleaved with XbaI and transferred into baculovirus transfer vector, pBacPAK9 and it was named pBacVP1 clone. The VP1 gene in the pBacVP1 clone was double-digested with SacI and PstI and then inserted just behind T5 phage promoter and the $6{\times}His$ region of the pQE-3D expression vector, and it was called pQEVPl. Again, the $6{\times}$His-tagged VP1 DNA fragment in the pQEVP1 was cleaved with EcoRI and transferred into the VP1 site of the pBacVP1, resulting pBacHis-VP1 recombinant. The pBacHis-VP1 DNA was cotransfected with LacZ-Hyphantria cunea nuclear polyhedrosis virus (LacZ-HcNPV) DNA digested with Bsu361 onto S. frugiperda cells to make a recombinant virus. One VP1-gene inserted recombinant virus was selected by plaque assay. The recombinant virus was named VP1-HcNPV-1. The $6{\times}$His-tagged VP1 protein produced by the pQEVP1 was purified with Ni-NTA resin chromatography and analyzed by SDS-PAGE and Western blot analysis. The molecular weight of the VP1 protein was 94 kDa. The recombinant virus, VP1-HcNPV-1 did not form polyhedral inclusion bodies and expressed VP1 protein with 95 kDa in the infected S. frugiperda cells, which was detected by Western blot. The titer of the VP1-HcNPV-1 in the first infected cells was $2.0{\times}10^5\;pfu/ml$ at 7 days postinfection.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.463-466
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• 2017

The purpose of this paper is to develop a module capable of all-directional driving different from conventional wheeled robots, and to solve the problems of the conventional mobile robot with side driving performance degradation, It is possible to overcome the disadvantages such as an increase in the time required for the unnecessary driving. The all - direction spherical wheel drive module for driving a ball - balancing robot is required to develop a power transfer mechanism and a driving algorithm for driving the robot in all directions using three rotor casters. 3DoF (Axis) A driver with built-in forward motion algorithm is embedded in the module and a driving motor module with 3DoF (axis) for driving direction and speed is installed. The movement mechanism depends on the sum of the rotation vectors of the respective driving wheels. It is possible to create various movement directions depending on the rotation and the vector sum of two or three drive wheels. It is possible to move in different directions according to the rotation vector field of each driving wheel. When a more innovative all-round spherical wheel drive module for forward movement is developed, it can be used in the driving part of the mobile robot to improve the performance of the robot more technically, and through the forward-direction robot platform with the drive module Conventional wheeled robots can overcome the disadvantage that the continuous straightening performance is lowered due to resistance to various environments. Therefore, it is necessary to use a full-direction driving function as well as a cleaning robot and a mobile robot applicable in the Americas and Europe It will be an essential technology for guide robots, boarding robots, mobile means, etc., and will contribute to the expansion of the intelligent service robot market and future automobile market.

• Tuberculosis and Respiratory Diseases
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• v.51 no.2
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• pp.122-134
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• 2001

Background : Some chemotherapeutic drugs induce NF-${\kappa}B$ activation by degrading the $I{\kappa}B{\alpha}$ protein in cancer cells which contributes to anticancer drug resistance. We hypothesized that inhibiting $I{\kappa}B{\alpha}$ degradation would block NF-${\kappa}B$ activation and result in increased tumor cell mortality in response to chemotherapy. Methods : The "superrepressor" form of the NF-${\kappa}B$ inhibitor was transferred by an adenoviral vector (Ad-$I{\kappa}B{\alpha}$-SR) to the human lung cancer cell lines (NCI H157 and NCI H460). With a MIT assay, the level of sensitization to cisplatin and paclitaxel were measured. To confirm the mechanism, an EMSA and Annexin V assay were performed. Results : EMSA showed that $I{\kappa}B{\alpha}$-SR effectively blocked the NF-${\kappa}B$ activation induced by cisplatin. Transduction with Ad-$I{\kappa}B{\alpha}$-SR resulted in an increased sensitivity of the lung cancer cell lines to cisplatin and paclitaxel by a factor of 2~3 in terms of $IC_{50}$. Annexin-V analysis suggests that this increment in chemosensitivity to cisplatin probably occurs through the induction of apoptosis. Conclusion : The blockade of chemotherapeutics induced NF-${\kappa}B$ activation by inducing Ad-$I{\kappa}B{\alpha}$-SR, increased apoptosis and increasing the chemosensitivity of the lung cancer cell lines tested, subsequently. Gene transfer of $I{\kappa}B{\alpha}$-SR appears to be a new therapeutic strategy of chemosensitization in lung cancer.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.3
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• pp.439-445
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• 2017

Objective: Production of alpha-1,3-galactosyltransferase (${\alpha}GT$)-deficient pigs is essential to overcome xenograft rejection in pig-to-human xenotransplantation. However, the production of such pigs requires a great deal of cost, time, and labor. Heterozygous ${\alpha}GT$ knockout pigs should be bred at least for two generations to ultimately obtain homozygote progenies. The present study was conducted to produce ${\alpha}GT$-deficient miniature pigs in much reduced time using mitotic recombination in neonatal ear skin fibroblasts. Methods: Miniature pig fibroblasts were transfected with ${\alpha}GT$ gene-targeting vector. Resulting gene-targeted fibroblasts were used for nuclear transfer (NT) to produce heterozygous ${\alpha}GT$ gene-targeted piglets. Fibroblasts isolated from ear skin biopsies of these piglets were cultured for 6 to 8 passages to induce loss of heterozygosity (LOH) and treated with biotin-conjugated IB4 that binds to galactose-${\alpha}$-1,3-galactose, an epitope produced by ${\alpha}GT$. Using magnetic activated cell sorting, cells with monoallelic disruption of ${\alpha}GT$ were removed. Remaining cells with LOH carrying biallelic disruption of ${\alpha}GT$ were used for the second round NT to produce homozygous ${\alpha}GT$ gene-targeted piglets. Results: Monoallelic mutation of ${\alpha}GT$ gene was confirmed by polymerase chain reaction in fibroblasts. Using these cells as nuclear donors, three heterozygous ${\alpha}GT$ gene-targeted piglets were produced by NT. Fibroblasts were collected from ear skin biopsies of these piglets, and homozygosity was induced by LOH. The second round NT using these fibroblasts resulted in production of three homozygous ${\alpha}GT$ knockout piglets. Conclusion: The present study demonstrates that the time required for the production of ${\alpha}GT$-deficient miniature pigs could be reduced significantly by postnatal skin biopsies and subsequent selection of mitotic recombinants. Such procedure may be beneficial for the production of homozygote knockout animals, especially in species, such as pigs, that require a substantial length of time for breeding.

• 한국공간정보시스템학회:학술대회논문집
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• 2004.12a
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• pp.169-181
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• 2004

SK Telecom has already constructed GIMS system as the base common framework of LBS/GIS service system based on OGC(OpenGIS Consortium)'s international standard for the first mobile vector map service in 2002, But as service content appears more complex, renovation has been needed to satisfy multi-purpose, multi-function and maximum efficiency as requirements have been increased. This research is for preparation ion of GML3-based platform to upgrade service from GML2 based GIMS system. And with this, it will be possible for variety of application services to provide location and geographic data easily and freely. In GML 3.0, it has been selected animation, event handling, resource for style mapping, topology specification for 3D and telematics services for mobile LBS multimedia service. And the schema and transfer protocol has been developed and organized to optimize data transfer to MS(Mobile Stat ion) Upgrade to GML 3.0-based GIMS system has provided innovative framework in the view of not only construction but also service which has been implemented and applied to previous research and system. Also GIMS channel interface has been implemented to simplify access to GIMS system, and service component of GIMS internals, WFS and WMS, has gotten enhanded and expanded function.

• Microbiology and Biotechnology Letters
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• v.26 no.2
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• pp.122-129
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• 1998

It were reported that antifungal mechanism of Enterobacter cloacae is a volatile ammonia that produced by the strain in soil, and the production of ammonia is related to the bacterial urease activity. A powerful bacterium SH14 against soil-borne pathogen Fusarium solani, which cause root rot of many important crops, was selected from a ginseng pathogen suppressive soil. The strain SH14 was identified as Bacillus subtilis by cultural, biochemical, morphological method, and $API^{circledR}$ test. From several in vitro tests, the antifungal substance that is produced from B. subtilis SH14 was revealed as heat-stable and low-molecular weight antibiotic substance. In order to construct the multifunctional biocontrol agent, the urease gene of Bacillus pasteurii which can produce pathogenes-suppressive ammonia transferred into antifungal bacterium. First, a partial BamH I digestion fragment of plasmid pBU11 containing the alkalophilic B. pasteurii l1859 urease gene was inserted into the BamH I site of pEB203 and expressed in Escherichia coli JM109. The recombinant plasmid was designated as pGU366. The plasmid pGU366 containing urease gene was introduced into the B. subtilis SH14 with PEG-induced protoplast transformation (PIP) method. The urease gene was very stably expressed in the transformant of B. subtilis SH14. Also, the optimal conditions for transformation were established and the highest transformation frequency was obtained by treatment of lysozyme for 90 min, and then addition of 1.5 ${mu}g$/ml DNA and 40% PEG4000. From the in vitro antifungal test against F. solani, antifungal activity of B. subtilis SH14(pGu366) containing urease gene was much higher than that of the host strain. Genetical development of B. subtilis SH14 by transfer of urease gene can be responsible for enhanced biocontrol efficacy with its antibiotic action.

• Asia pacific journal of information systems
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• v.18 no.3
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• pp.123-145
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• 2008

Recently, as the information communication technology develops, the discussion regarding the ubiquitous environment is occurring in diverse perspectives. Ubiquitous environment is an environment that could transfer data through networks regardless of the physical space, virtual space, time or location. In order to realize the ubiquitous environment, the Pervasive Sensing technology that enables the recognition of users' data without the border between physical and virtual space is required. In addition, the latest and diversified technologies such as Context-Awareness technology are necessary to construct the context around the user by sharing the data accessed through the Pervasive Sensing technology and linkage technology that is to prevent information loss through the wired, wireless networking and database. Especially, Pervasive Sensing technology is taken as an essential technology that enables user oriented services by recognizing the needs of the users even before the users inquire. There are lots of characteristics of ubiquitous environment through the technologies mentioned above such as ubiquity, abundance of data, mutuality, high information density, individualization and customization. Among them, information density directs the accessible amount and quality of the information and it is stored in bulk with ensured quality through Pervasive Sensing technology. Using this, in the companies, the personalized contents(or information) providing became possible for a target customer. Most of all, there are an increasing number of researches with respect to recommender systems that provide what customers need even when the customers do not explicitly ask something for their needs. Recommender systems are well renowned for its affirmative effect that enlarges the selling opportunities and reduces the searching cost of customers since it finds and provides information according to the customers' traits and preference in advance, in a commerce environment. Recommender systems have proved its usability through several methodologies and experiments conducted upon many different fields from the mid-1990s. Most of the researches related with the recommender systems until now take the products or information of internet or mobile context as its object, but there is not enough research concerned with recommending adequate store to customers in a ubiquitous environment. It is possible to track customers' behaviors in a ubiquitous environment, the same way it is implemented in an online market space even when customers are purchasing in an offline marketplace. Unlike existing internet space, in ubiquitous environment, the interest toward the stores is increasing that provides information according to the traffic line of the customers. In other words, the same product can be purchased in several different stores and the preferred store can be different from the customers by personal preference such as traffic line between stores, location, atmosphere, quality, and price. Krulwich(1997) has developed Lifestyle Finder which recommends a product and a store by using the demographical information and purchasing information generated in the internet commerce. Also, Fano(1998) has created a Shopper's Eye which is an information proving system. The information regarding the closest store from the customers' present location is shown when the customer has sent a to-buy list, Sadeh(2003) developed MyCampus that recommends appropriate information and a store in accordance with the schedule saved in a customers' mobile. Moreover, Keegan and O'Hare(2004) came up with EasiShop that provides the suitable tore information including price, after service, and accessibility after analyzing the to-buy list and the current location of customers. However, Krulwich(1997) does not indicate the characteristics of physical space based on the online commerce context and Keegan and O'Hare(2004) only provides information about store related to a product, while Fano(1998) does not fully consider the relationship between the preference toward the stores and the store itself. The most recent research by Sedah(2003), experimented on campus by suggesting recommender systems that reflect situation and preference information besides the characteristics of the physical space. Yet, there is a potential problem since the researches are based on location and preference information of customers which is connected to the invasion of privacy. The primary beginning point of controversy is an invasion of privacy and individual information in a ubiquitous environment according to researches conducted by Al-Muhtadi(2002), Beresford and Stajano(2003), and Ren(2006). Additionally, individuals want to be left anonymous to protect their own personal information, mentioned in Srivastava(2000). Therefore, in this paper, we suggest a methodology to recommend stores in U-market on the basis of ubiquitous environment not using personal information in order to protect individual information and privacy. The main idea behind our suggested methodology is based on Feature Matrices model (FM model, Shahabi and Banaei-Kashani, 2003) that uses clusters of customers' similar transaction data, which is similar to the Collaborative Filtering. However unlike Collaborative Filtering, this methodology overcomes the problems of personal information and privacy since it is not aware of the customer, exactly who they are, The methodology is compared with single trait model(vector model) such as visitor logs, while looking at the actual improvements of the recommendation when the context information is used. It is not easy to find real U-market data, so we experimented with factual data from a real department store with context information. The recommendation procedure of U-market proposed in this paper is divided into four major phases. First phase is collecting and preprocessing data for analysis of shopping patterns of customers. The traits of shopping patterns are expressed as feature matrices of N dimension. On second phase, the similar shopping patterns are grouped into clusters and the representative pattern of each cluster is derived. The distance between shopping patterns is calculated by Projected Pure Euclidean Distance (Shahabi and Banaei-Kashani, 2003). Third phase finds a representative pattern that is similar to a target customer, and at the same time, the shopping information of the customer is traced and saved dynamically. Fourth, the next store is recommended based on the physical distance between stores of representative patterns and the present location of target customer. In this research, we have evaluated the accuracy of recommendation method based on a factual data derived from a department store. There are technological difficulties of tracking on a real-time basis so we extracted purchasing related information and we added on context information on each transaction. As a result, recommendation based on FM model that applies purchasing and context information is more stable and accurate compared to that of vector model. Additionally, we could find more precise recommendation result as more shopping information is accumulated. Realistically, because of the limitation of ubiquitous environment realization, we were not able to reflect on all different kinds of context but more explicit analysis is expected to be attainable in the future after practical system is embodied.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.